リーマン多様体上のフローマッチングによるロボット動作学習ポリシー
Riemannian Flow Matching Policy for Robot Motion Learning
フローマッチングをリーマン多様体上に拡張し、ロボットの視覚運動ポリシーを効率的に学習・生成する手法RFMPを提案。拡散ポリシーより滑らかな軌道と高速推論を実現。
著者: Max Braun, Noémie Jaquier, Leonel Rozo, Tamim Asfour
分類: cs.RO, cs.LG
原文アブストラクト
We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference capabilities of flow matching methods. By design, RFMP inherits the strengths of flow matching: the ability to encode high-dimensional multimodal distributions, commonly encountered in robotic tasks, and a very simple and fast inference process. We demonstrate the applicability of RFMP to both state-based and vision-conditioned robot motion policies. Notably, as the robot state resides on a Riemannian manifold, RFMP inherently incorporates geometric awareness, which is crucial for realistic robotic tasks. To evaluate RFMP, we conduct two proof-of-concept experiments, comparing its performance against Diffusion Policies. Although both approaches successfully learn the considered tasks, our results show that RFMP provides smoother action trajectories with significantly lower inference times.
